Yearly Traffic Safety Analysis

146 CRASHES IN
IOWA, IA
2015

In 2015, Chickasaw County recorded 146 traffic crashes, which resulted in 1 fatality and 35 injuries. A significant portion of these incidents, 44.5%, were attributed to collisions or events involving animals, making it the most notable statistical finding in the dataset.

146

Total Crash Events

1

Persons Killed

35

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Aggregate counts from crash, person, and vehicle records

Vulnerable Road User Casualties

In 2015, motorists comprised the vast majority of casualties, with 1 motorist killed and 34 injured in traffic crashes. There were no pedestrian fatalities or injuries recorded. One cyclist was injured during the year.

0

Cyclists Killed

1

Motorists Killed

1

Cyclists Injured

34

Motorists Injured

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Crashes in Chickasaw County occurred most frequently on Mondays, with 30 incidents reported, and during the 5 p.m. hour, which saw 14 crashes. Analysis by month shows a peak in November with 25 crashes. A notable pattern emerges from lighting conditions, where 35 crashes (24.0% of the total) happened after dark on unlit roadways.

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The majority of crashes, 117 out of 146 (80.1%), resulted in no injuries and were limited to property damage. There were 28 crashes involving minor, serious, or possible injuries. One crash was fatal, resulting in one fatality.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.7%
Serious Injury4serious injury crashes2.7%
Minor Injury15minor injury crashes10.3%
Possible Injury9possible injury crashes6.2%
No Injury117no injury crashes80.1%

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Most severe injury per crash record

Top Contributing Factors

The most frequently cited contributing factor in crashes was 'Animal,' which was involved in 65 incidents, accounting for 44.5% of all crashes. Following this, 'Lost Control' was noted in 12 crashes (8.2%), and 'Ran off road - left' was a factor in 8 crashes (5.5%).

Officer-Reported Primary Contributing Cause

Animal65 (44.5%)
Lost Control12 (8.2%)
Ran off road - left8 (5.5%)
Swerving/Evasive Action5 (3.4%)
Ran off road - straight5 (3.4%)
Driving too fast for conditions4 (2.7%)
FTYROW: From stop sign4 (2.7%)
Ran Stop Sign4 (2.7%)
FTYROW: At uncontrolled intersection3 (2.1%)
Improper or erratic lane changing3 (2.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

A significant number of crashes occurred in seemingly ideal conditions, with 53.4% (78 crashes) on dry roads and 47.3% (69 crashes) in clear weather. Crashes during daylight hours accounted for 45.9% (67) of the total. Conversely, adverse conditions were also present, with 35 crashes (24.0%) occurring on unlit dark roadways and 13 crashes (8.9%) on snow-covered surfaces.

Weather

Clear69 (63.3%)
Cloudy26 (23.9%)
Rain6 (5.5%)
Snow4 (3.7%)
Blowing Snow3 (2.8%)
Fog, smoke, smog1 (0.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Weather condition at time of crash

Lighting

Daylight67 (59.8%)
Dark - roadway not lighted35 (31.3%)
Dark - roadway lighted4 (3.6%)
Dusk4 (3.6%)
Dawn2 (1.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Lighting condition field

Road Surface

Dry78 (69.6%)
Snow13 (11.6%)
Wet12 (10.7%)
Ice/frost4 (3.6%)
Gravel4 (3.6%)
Mud, dirt1 (0.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Road surface condition field

Vehicles & Demographics

Analysis of persons involved in crashes shows the most represented age groups were 45-54 years old (42 individuals) and 55-64 years old (41 individuals). Among the 208 vehicles involved, the most frequent makes were Chevrolet (39 vehicles), Ford (29 vehicles), and Dodge (15 vehicles).

Top Vehicle Makes (208 vehicles)

1
CHEVROLET39 (18.8%)
2
FORD29 (13.9%)
3
DODGE15 (7.2%)
4
GMC14 (6.7%)
5
PONTIAC12 (5.8%)
6
BUICK11 (5.3%)
7
CHEV10 (4.8%)
8
CHRYSLER7 (3.4%)
9
FREIGHTLINER7 (3.4%)
10
TOYOTA6 (2.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records

15 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (191 persons with recorded sex)

Male126 (66.0%)
Female65 (34.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Person-level records linked to crash events

Major Cause

The primary major cause identified in crash reports was 'Animal,' cited in 65 incidents, or 44.5% of the total. Other significant causes included 'Lost Control,' which was a factor in 12 crashes (8.2%), and 'Ran off road - left,' noted in 8 crashes (5.5%).

Major Cause

1
Animal65 (45.8%)
2
Lost Control12 (8.5%)
3
Ran off road - left8 (5.6%)
4
Swerving/Evasive Action5 (3.5%)
5
Ran off road - straight5 (3.5%)
6
Driving too fast for conditions4 (2.8%)
7
FTYROW: From stop sign4 (2.8%)
8
Ran Stop Sign4 (2.8%)
9
FTYROW: At uncontrolled intersection3 (2.1%)

Showing top 9 of 29 reported. 20 additional (32 total) not shown: Improper or erratic lane changing, Ran Traffic Signal, FTYROW: Making left turn, Followed too close, Improper Backing, FTYROW: From driveway, Driver Distraction: Other interior distraction, Driver Distraction: Passenger, Driver Distraction: Inattentive/lost in thought, Crossed centerline (undivided), Exceeded authorized speed, Failed to keep in proper lane, Disregarded RR Signal, FTYROW: From yield sign, FTYROW: Other (explain in narrative), Operating vehicle in an reckless, erratic, careless, negligent manner, Other (explain in narrative): No improper action, Other (explain in narrative): Other, Other (explain in narrative): Vision obstructed, Passing: Other passing (explain in narrative).

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

First Harmful Event

The most common first harmful event was a 'Collision with: Animal,' recorded in 64 crashes. The second most frequent event was a 'Collision with: Vehicle in traffic,' which occurred in 40 crashes. Collisions with fixed objects, such as a ditch or tree, and non-collision events like rollovers were less frequent.

First Harmful Event

1
Collision with: Animal64 (50%)
2
Collision with: Vehicle in traffic40 (31.3%)
3
Collision with: Parked motor vehicle3 (2.3%)
4
Non-collision events: Overturn/rollover3 (2.3%)
5
Non-collision events: Other non-collision (explain in narrative)3 (2.3%)
6
Collision with fixed object: Ditch3 (2.3%)
7
Non-collision events: Vehicle went airborne2 (1.6%)
8
Collision with fixed object: Tree2 (1.6%)
9
Other (explain in narrative)1 (0.8%)

Showing top 9 of 16 reported. 7 additional (7 total) not shown: Collision with fixed object: Bridge/bridge rail parapet, Collision with fixed object: Culvert/pipe opening, Collision with fixed object: Other fixed object (explain in narrative), Collision with: Non-motorist (see non-motorist section - NOT a unit), Collision with: Railway vehicle/train, Miscellaneous events: Eluding law enforcement, Miscellaneous events: Hit and run.

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Roadway Junction / Feature

Crashes were most likely to occur on non-intersection road segments, which accounted for 67 incidents. Four-way intersections were the most common junction type for crashes, with 16 occurrences, followed by T-intersections with 9 crashes. Incidents related to driveway access were noted in 9 crashes.

Roadway Junction / Feature

1
Non-intersection: Non-junction/no special feature67 (60.9%)
2
Intersection: Four-way intersection16 (14.5%)
3
Intersection: T-intersection9 (8.2%)
4
Non-intersection: Driveway access (related, not in)7 (6.4%)
5
Interchange-related: Other interchange (explain in narrative)3 (2.7%)
6
Non-intersection: Driveway access (within)2 (1.8%)
7
Non-intersection: Railroad grade crossing2 (1.8%)
8
Intersection: Traffic circle1 (0.9%)
9
Intersection: Intersection with ramp1 (0.9%)

Showing top 9 of 11 reported. 2 additional (2 total) not shown: Non-intersection: Bike lanes, Intersection: Other intersection (explain in narrative).

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Vehicle Type

Passenger cars were the most common vehicle type involved in crashes, with 77 units recorded. Light trucks and pickups were the second most frequent with 48 units, followed by Sport Utility Vehicles with 36 units. Commercial tractor-trailers were involved in 7 instances, and motorcycles were involved in 3.

Vehicle Type

"Other" combines 10 smaller categories (14 records): Motorcycle (3), Maintenance/construction vehicle (2), Cargo/panel van (2), Farm tractor (1), Golf cart (1), School bus (seats > 15) (1), Tractor/doubles (1), Train (1), Truck/trailer (1), All-terrain vehicle (ATV) (1).

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records

Traffic Control Device

The vast majority of crashes, 127 out of 146, occurred in areas with no traffic controls present. In locations with controls, stop signs were associated with 17 crashes and traffic signals with 15 crashes.

Traffic Control Device

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records

Most Damaged Area

Frontal impacts were the most common area of vehicle damage, with 50 vehicles sustaining damage to the front and an additional 31 to the front corners. Rear-end damage was recorded for 14 vehicles, while various side impacts were also frequently documented.

Most Damaged Area

"Other" combines 9 smaller categories (41 records): Driver side - middle (11), Rear - driver side corner (8), Passenger side - middle (6), Passenger side - rear (6), Rear - passenger side corner (5), Top (2), Other (explain in narrative) (1), Non-collision/no damage (1), Cargo loss (1).

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records

Crashes by City

Within Chickasaw County, the city of New Hampton recorded the highest volume of crashes with 44 incidents. The city of Nashua had the second-highest count with 27 crashes, followed by Fredericksburg with 4 crashes.

Crashes by City

1
NEW HAMPTON44 (50.6%)
2
NASHUA27 (31%)
3
FREDERICKSBURG4 (4.6%)
4
ALTA VISTA3 (3.4%)
5
BASSETT3 (3.4%)
6
IONIA2 (2.3%)
7
PROTIVIN1 (1.1%)
8
FORT MADISON1 (1.1%)
9
LAWLER1 (1.1%)

Showing top 9 of 10 reported. 1 additional (1 total) not shown: NORTH WASHINGTON.

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Paved vs Unpaved Road

Of the crashes where road surface type was recorded, 126 occurred on paved roads. A notable portion, 16 crashes or approximately 11.3% of those with known surface types, took place on unpaved surfaces such as gravel or dirt roads.

Paved vs Unpaved Road

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Roadway Contributing Factor

A roadway factor was identified as a contributor in a minority of crashes. 'Surface condition,' such as wet or icy roads, was cited in 15 incidents. 'Work Zone' related factors were noted in 2 crashes.

Roadway Contributing Factor

1
Surface condition (e.g.wet, icy)15 (88.2%)
2
Work Zone (roadway-related)2 (11.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Property Damage

The most common estimated property damage cost was in the $1,500 to $7,500 range, accounting for 113 crashes. A smaller number of incidents resulted in higher costs, with 27 crashes causing between $7,500 and $25,000 in damage, and 3 crashes exceeding $25,000 in damage.

Property Damage

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Manner of Collision

Single-vehicle, non-collision events were the dominant manner of collision, comprising 83 crashes or 56.8% of the total. Among multi-vehicle crashes, broadside collisions were most frequent with 18 incidents (12.3%), closely followed by rear-end collisions with 17 incidents (11.6%).

Manner of Collision

"Other" combines 1 smaller categories (1 records): Other (explain in narrative) (1).

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Pre-Crash Driver Action

The most common pre-crash action for vehicles involved was 'Movement essentially straight,' recorded for 131 of the 208 vehicles. Other frequent actions included 'Turning left' (12 vehicles) and being 'Stopped in traffic' (10 vehicles).

Pre-Crash Driver Action

1
Movement essentially straight131 (69.3%)
2
Turning left12 (6.3%)
3
Stopped in traffic10 (5.3%)
4
Backing9 (4.8%)
5
Legally Parked8 (4.2%)
6
Slowing/stopping (deceleration)4 (2.1%)
7
Turning right4 (2.1%)
8
Negotiating a curve3 (1.6%)
9
Overtaking/passing3 (1.6%)

Showing top 9 of 12 reported. 3 additional (5 total) not shown: Changing lanes, Other (explain in narrative), Accelerating in road.

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Vehicle unit records

Person Type

Of the 244 individuals involved in crashes, the vast majority, 234 people (95.9%), were drivers. Passengers accounted for 9 individuals, and one person was identified as a bicyclist.

Person Type

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Person Injury Severity

Across all individuals involved in crashes, there was 1 fatality recorded. In addition, 6 people sustained serious injuries, 17 had minor injuries, and 12 had possible injuries, for a total of 35 injured persons.

Person Injury Severity

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Occupant Safety Equipment

Among the participants for whom safety equipment use was documented, 22 were recorded as using a shoulder and lap belt. Six individuals were recorded as using no safety equipment.

Occupant Safety Equipment

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Person-level records linked to crash events

Vehicles Per Crash

Single-vehicle crashes were the most common type, accounting for 85 of the 146 total incidents (58.2%). Two-vehicle collisions were also frequent with 60 occurrences, while only one crash involved three vehicles.

Vehicles Per Crash

Source: Iowa Crash Data · ArcGIS Open Data · 2015-01-01 to 2015-12-31 · Crash-level records

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Iowa Crash Data, accessed programmatically via the ArcGIS Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.

Data Retrieval

  • Access method: ArcGIS Open Data API (SoQL queries)
  • Data format: Structured JSON via REST API
  • Record types queried: Crash events, person records, and vehicle unit records
  • Date filter applied: 2015-01-01 through 2015-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2015-01-01 through 2015-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 146
  • Total persons involved: 244
  • Total vehicles involved: 208

Analytical Methodology

  • Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
  • Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
  • Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
  • Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
  • Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
  • Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
  • AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.

Limitations & Disclaimers

  • Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
  • Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
  • Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
  • AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
  • Percentages are calculated from reported data and are subject to rounding.

Non-Affiliation Disclosure

This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.

Data License

The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.

Corrections & Feedback

If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.

Suggested Citation

ThatCarHitMe.com (Injuria.ai). "iowa, IA Crash Intelligence Report: 2015." Published September 9, 2026. Reporting period: 2015-01-01 to 2015-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2015-annual-report

About the Publisher

ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.

Questions about this report's data or methodology: data@injuria.ai

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Chickasaw County, IA Crash Report — 2015 | ThatCarHitMe.com